Binary Classification on French Hospital Data: Benchmark of 7 Machine Learning Algorithms

Oliveira, Hugo De; Prodel, Martin; Augusto, Vincent

doi:10.1109/smc.2018.00301

Cited by 13 publications

(11 citation statements)

References 20 publications

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“…We have considered and compared the six most popular machine learning approaches [12,13] which are Decision Tree (DT) [14], Random Forest (RF) [15], Naive Bayes (NB) [16], Logistic regression (LR) [17], Support Vector Machine (SVM) [18], Artificial Neural Network (ANN) [19]. All of them are supervised learning algorithms, i.e., require a training phase with labelled data.…”

Section: Machine Learning Algorithmsmentioning

confidence: 99%

On the Efficiency of Machine Learning Models in Malaria Prediction

Mbaye

2021

Studies in Health Technology and Informatics

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Malaria is still a real public health concern in Sub-Saharan African countries such as Senegal where it represents approximately 35% of the consultation activities in the hospitals. This is mainly due to the lack of appropriate medical care support and often late and error-prone diagnosis of the disease. For instance, largely used tools like Rapid Diagnosis Test are not fully reliable. This study proposes an extensive study of the efficiency of the most popular machine learning models for the task of Malaria occurrence prediction. We have considered patients from Senegal and have evaluated the overall accuracy of each considered algorithm based on sign and symptom information. Our main result is that machine learning algorithms are promising, in particular Naive Bayesian presents a recall very close to that of a rapid diagnostic test while improving highly its precision by 9%.

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Section: Machine Learning Algorithmsmentioning

confidence: 99%

On the Efficiency of Machine Learning Models in Malaria Prediction

Mbaye

2021

Studies in Health Technology and Informatics

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“…A machine learning model using decision tree analysis was built on Python software to study the factors associated with the risk of having at least one rehospitalisation for AECOPD within six months of the index hospitalisation using a binary splitting decision tree algorithm. 17,25 The resulting tree is represented as a "sunburst plot".…”

Section: Decision Tree Analysismentioning

confidence: 99%

<p>Identification of Patient Profiles with High Risk of Hospital Re-Admissions for Acute COPD Exacerbations (AECOPD) in France Using a Machine Learning Model</p>

Cavaillès¹,

Melloni²,

Motola³

et al. 2020

COPD

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Purpose: To characterise patients with chronic obstructive pulmonary disease (COPD) who are rehospitalised for an acute exacerbation, to estimate the cost of these hospitalisations, to characterise high risk patient sub groups and to identify factors potentially associated with the risk of rehospitalisation. Patients and Methods: This was a retrospective study using the French National Hospital Discharge Database. All patients aged ≥40 years hospitalised for an acute exacerbation of COPD between 2015 and 2016 were identified and followed for six months. Patients with at least one rehospitalisation for acute exacerbation of COPD constituted the rehospitalisation analysis population. A machine learning model was built to study the factors associated with the risk of rehospitalisation using decision tree analysis. A direct cost analysis was performed from the perspective of national health insurance. Results: A total of 143,006 eligible patients were hospitalised for an acute exacerbation of COPD (AECOPD) in 2015-2016 (mean age: 74 years; 62.1% men). 25,090 (18.8%) were rehospitalised for another exacerbation within six months. In this study, 8.5% of patients died during or immediately following the index hospitalisation and 10.5% died during or immediately after rehospitalisation (p <0.001). The specific cost of these rehospitalisations was € 5304. The overall total cost per patient of all AECOPD-related stays was € 9623, being significantly higher in patients who were rehospitalised (€ 16,275) compared to those who were not (€ 8208). In decision tree analysis, the most important driver of rehospitalisation was hospitalisation in the previous two years (contributing 85% of the information). Conclusion: Rehospitalisations for acute exacerbations of COPD carry a high epidemiological and economic burden. Since hospitalisation for an acute exacerbation is the most important determinant of future rehospitalisations, management of COPD needs to focus on interventions aimed at decreasing the rehospitalisation risk of in order to lower the burden of disease.

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“…The SNDS is exhaustive for the NHI and was proven reliable to conduct epidemiological studies [ 13 ]. The use of machine learning technics to predict clinical outcomes from such electronic health records or claim data provides is an everincreasing topic of interest for decision makers [ [14][15][16][17][18][19][20] ].…”

Section: Introductionmentioning

confidence: 99%

“…13 The use of machine learning technics to predict clinical outcomes from such electronic health records or claim data provides is an ever-increasing topic of interest for decision makers. 14–20…”

Section: Introductionmentioning

confidence: 99%

Costs and Mortality Associated with HIV: A Machine Learning Analysis of the French National Health Insurance Database

Prodel¹,

Finkielsztejn

Roustand

et al. 2022

Journal of Public Health Research

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Background: The objective is to characterise the economic burden to the healthcare system of people living with HIV (PLWHIV) in France and to help decision makers in identifying risk factors associated with high-cost and high mortality profiles.Design and Method: The study is a retrospective analysis of PLWHIV identified in the French National Health Insurance database (SNDS). All PLWHIV present in the database in 2013 were identified. All healthcare resource consumption from 2008 to 2015 inclusive was documented and costed (for 2013 to 2015) from the perspective of public health insurance. High-cost and high mortality patient profiles were identified by a machine learning algorithm.Results: In 2013, 96,423 PLWHIV were identified in the SNDS database, including 3,373 incident cases. Overall, 3,224 PLWHIV died during the three-year follow-up period (mean annual mortality rate: 1.1%). The mean annual per capita cost incurred by PLWHIV was € 14,223, corresponding to a total management cost of HIV of € 1,370 million in 2013. The largest contribution came from the cost of antiretroviral medication (M€ 870; 63%) followed by hospitalisation (M€ 154; 11%). The costs incurred in the year preceding death were considerably higher. Four specific patient profiles were identified for under/over-expressing these costs, suggesting ways to reduce them.Conclusion: Even though current therapeutic regimens provide excellent virological control in most patients, PLWHIV have excess mortality. Other factors such as comorbidities, lifestyle factors and screening for cancer and cardiovascular disease, need to be targeted in order to lower the mortality and cost associated with HIV infection.

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Binary Classification on French Hospital Data: Benchmark of 7 Machine Learning Algorithms

Cited by 13 publications

References 20 publications

On the Efficiency of Machine Learning Models in Malaria Prediction

On the Efficiency of Machine Learning Models in Malaria Prediction

<p>Identification of Patient Profiles with High Risk of Hospital Re-Admissions for Acute COPD Exacerbations (AECOPD) in France Using a Machine Learning Model</p>

Costs and Mortality Associated with HIV: A Machine Learning Analysis of the French National Health Insurance Database

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